Scouting Firefly Algorithm and its Performance on Global Optimization Problems

For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, standard firefly algorithm (FA) posted some limitations in its process that can eventually lead to premature convergence, affecting performance adding uncertainty optimization results. To address these constraints, this study introduces an additional novel search mechanism for FA inspired by behavior of scout bee artificial colony (ABC) algorithm, termed "Scouting FA". Specifically, fireflies stuck local optima will take directed extra random walks escape toward region optimum solution, thus improving convergence accuracy. Empirical findings on five benchmark functions have validated effects modification revealed Scouting is superior original version.